How to Start a Career in AI in 2026: Skills, Jobs and Certifications

Artificial Intelligence is changing how businesses operate, and it is also creating new opportunities for people with a wide range of technical and professional backgrounds.

If you're considering a career in AI, knowing where to start can be difficult. Do you need to learn how to code? Should you study data science? Which AI certification is worth taking? And do you need a degree to get an AI job?

The answer depends on the type of AI career you want.

There is no single route into artificial intelligence. Different roles such as AI engineers, data scientists, developers, cybersecurity specialists and governance professionals all need different skills and qualifications.

This guide explains the main AI career paths, the skills you'll need, and how certifications can help you move towards your chosen role.

What Does a Career in AI Look Like in 2026?

When people think about an AI career, they often picture a machine learning engineer developing complex algorithms. In reality, the AI industry encompasses a much wider range of roles.

While this will include some professionals directly responsible for the development of AI models and applications, there are many others throughout an organisation who have different AI-related objectives. Still within the realm of engineering will be those who ensure AI models are enabled with high-quality data, or that AI infrastructure is hardened against the threat of cyber attack. Other professionals will be focused on business adoption to ensure AI systems are robustly implemented, processes and workflows optimised to include agentic AI capability, and the project management of AI change transformation is on track. Finally, still others will take on more human-centric AI roles to consider the ethics, safety, and usability of human/AI interaction.

Firebrand's current AI training portfolio reflects this breadth, spanning AI engineering, data science, AI agents, security, governance and AI fundamentals. Check out the Top AI Courses in 2026 for a broader overview of the certifications and training available.

AI Engineer

AI engineers design, build and deploy AI-powered applications and services. Their work can involve machine learning, generative AI, APIs, cloud platforms and AI services.

Key skills: Python, machine learning, cloud computing, AI APIs, application development and deployment.

Machine Learning Engineer

Machine learning engineers take models from development into production. They work with data scientists and data engineers to train, deploy, monitor and maintain machine learning systems.

Key skills: Python, statistics, machine learning, data engineering, cloud platforms and MLOps.

Data Scientist

Data scientists use data to identify patterns, answer business questions and develop predictive or machine learning models.

Key skills: Python, statistics, data analysis, machine learning and data visualisation.

Generative AI or AI Developer

Generative AI developers build applications around technologies such as large language models (LLMs). This can include chatbots, retrieval-augmented generation (RAG) systems and AI agents.

Key skills: Python, APIs, LLMs, prompt engineering, RAG, vector databases and cloud development.

AI Security Specialist

As organisations deploy more AI systems, they also need professionals who understand how to protect them.

AI security can involve identifying vulnerabilities, managing AI-related risks and defending systems against increasingly sophisticated attacks.

Key skills: cybersecurity, cloud security, AI systems, threat modelling and risk management.

AI Governance and Responsible AI Specialist

Not every AI career involves coding. Governance professionals help organisations manage AI risks, comply with regulations and develop responsible approaches to using AI.

Key skills: AI literacy, risk management, governance, data protection, ethics and regulation.

AI Transformation Professional

Businesses also need people who can identify where AI can create value and help teams implement it effectively.

Key skills: AI literacy, business strategy, change management, responsible AI and project management.

The important point is that an AI career is not one job. Your first decision should be to identify which part of the AI ecosystem you want to work in.

What Skills Do You Need to Work in AI?

Once you've chosen a direction, you can work backwards to identify the skills you need.

• Programming: For most technical AI careers, Python is one of the most valuable languages to learn. It is widely used across data science, machine learning and AI application development.
• Data: AI depends on data, making data skills fundamental to many AI careers. Useful skills include SQL, data analysis, statistics, data preparation and visualisation, etc.
• Machine Learning: Technical AI professionals should understand the fundamentals of supervised and unsupervised learning, machine learning algorithms, neural networks, natural language processing, model evaluation, large language models, etc.
• Generative AI and AI Agents: For people entering AI application development, understanding how modern generative AI systems are built and deployed is increasingly valuable. That can include prompt engineering, AI APIs, vector search, and more.
• Cloud Computing: Many AI systems run in the cloud, so cloud knowledge can be an important part of an AI engineer, developer or data professional's toolkit. Azure, AWS and Google Cloud all provide infrastructure and services for AI workloads.
• Responsible AI and Security: AI professionals increasingly need to understand data privacy, security, regulatory requirements, responsible AI development.
   

Which AI Certifications Do You Need?

There is no single AI certification that you need for every AI job.

Instead, choose certifications based on the role you want to pursue. For example, Azure AI Apps and Agents Developer Associate (AI-103) is the current successor to the retired AI-102 and is designed for professionals building AI applications and agents on Azure with Microsoft Foundry, while Azure AI Cloud Developer (AI-200) focuses on the application and cloud infrastructure needed to create, monitor and troubleshoot AI solutions.

For a data-focused career, the Azure MLOps Engineer Associate (AI-300) covers areas including model training, deployment, experimentation, optimisation and monitoring, the successor to the retired Data Scientist Associate (DP-100).

Here are other relevant trainings and certifications based on the specialisation you want in AI.

Target career	Relevant training
AI Engineer	Azure AI Apps and Agents Developer Associate (AI-103), Azure AI Cloud Developer (AI-200)
AI / Generative AI Developer	AI-200, AI-103, Develop AI Agents on Azure (AI-3026)
Data Scientist / ML professional	Azure MLOps Engineer Associate (AI-300), Python and data training
Data Engineer for AI	Azure Data Engineer Associate (DP-203), data fundamentals
AI Security Specialist	AI security and cybersecurity certifications
AI Governance Professional	AI governance, responsible AI and risk certifications
AI beginner	Azure AI Fundamentals (AI-900), BCS AI qualifications
AI Transformation Professional	AI fundamentals and Microsoft AI Transformation Leader

 

Do you need an AI certification to get an AI job?

Certifications can demonstrate that you have learned a recognised set of skills, but they are only one part of your professional profile.

For technical AI roles, employers may also look for:

• programming ability
• practical projects
• experience with cloud platforms
• data and machine learning knowledge
• problem-solving ability
• communication skills
• evidence that you can apply your knowledge

This is why you shouldn't approach AI certifications as a checklist. Choose the qualification that supports the career you want to build.

This top AI courses guide provides further context on the different AI certifications available across organisations, including BCS, CertNexus, IAPP, ISACA, ISTQB and Microsoft.

How to Start a Career in AI: Your Step-by-Step Roadmap

Once you know which direction you want to take, you can build your AI career step by step.

Step 1: Learn the fundamentals

Start by understanding the basic principles of AI, data and cloud computing.

If you're completely new to AI, an introductory qualification such as Microsoft Azure AI Fundamentals or a BCS AI qualification can provide a useful starting point. Firebrand's BCS Essentials Certificate in AI, for example, is designed for professionals interested in implementing AI and has no formal prerequisites.

If you're targeting a technical career, begin developing Python and data skills alongside your AI fundamentals.

Step 2: Choose your specialisation

Don't try to learn everything at once. You can choose a target pathway such as:

• AI Engineering → Python + AI + cloud + application development
• Data Science → Python + statistics + data + machine learning
• AI Development → Python + APIs + LLMs + cloud + AI agents
• AI Security → cybersecurity + cloud + AI security
• AI Governance → AI + risk + regulation + responsible AI

Your existing experience can help determine which route makes the most sense.

Step 3: Build the skills for your target role

Once you've chosen a pathway, focus your training.

For example, an aspiring AI engineer might progress from AI fundamentals into Azure AI and application development. Someone moving from data analysis towards machine learning might prioritise Python, statistics and data science.

You don't need to master the entire AI landscape before applying for your first role. Build the skills that are relevant to the job you actually want.

Step 4: Gain an industry-recognised certification

A certification can give employers a straightforward way to verify your knowledge.

The key is to match the certification to the role rather than collecting as many credentials as possible.
Firebrand's Top AI Courses in 2026 can help you compare different training options.

Step 5: Build practical projects

Don't stop at the certificate. You need to create projects that demonstrate what you can actually do.

Depending on your career path, this could include:

• a generative AI application
• an AI chatbot
• a RAG application
• a machine learning model
• a data pipeline
• an AI agent
• an AI security assessment
• an AI governance framework

A portfolio gives you something concrete to discuss during applications and interviews.

Step 6: Move into your first AI role and then specialise

Your first role doesn't have to have "AI" in the job title.

A software developer can move into AI application development. A data analyst can progress towards data science. A cybersecurity professional can specialise in AI security.

The fastest route into AI may therefore be building on the skills you already have, rather than starting your career again from scratch.

For more advice on transitioning into technology, read How to Break Into Tech (Fast), which covers certifications, practical experience, portfolios and career-changing routes.

AI Career FAQs

Do you need a degree to work in AI?

Not necessarily. Requirements vary significantly between AI roles. Research-focused positions may require advanced academic qualifications, while many applied technology roles place greater emphasis on technical skills, experience, certifications and demonstrable projects.

Can you work in AI without coding?

Yes. AI governance, risk, compliance, transformation and some consulting roles may not require extensive programming. However, if you want to build AI systems, applications or machine learning models, programming skills will be important.

Is Python necessary for an AI career?

Python is not required for every AI-related role, but it is one of the most useful languages to learn if you want to work in AI engineering, machine learning or data science.

What is the best AI certification?

There is no single best AI certification. The right choice depends on your career goal. AI engineers may benefit from Azure AI Apps and Agents (AI-103) or Azure AI Cloud Developer (AI-200) certifications, data scientists from MLOps and machine learning certifications such as AI-300, and AI governance professionals from governance and risk qualifications.

How long does it take to start a career in AI?

There is no standard timeframe. Someone with existing software development or data experience may be able to transition into AI relatively quickly, while a complete beginner will need more time to build foundational technical skills.

The important thing is to focus on a clear career pathway rather than trying to learn every area of AI at once.

Is AI a good career choice in 2026 and beyond?

AI is creating opportunities across engineering, data, software development, cybersecurity, governance and business transformation. Rather than focusing solely on becoming an AI expert, consider which part of the AI ecosystem matches your existing skills and interests.

Ready to build your career in AI?

From AI fundamentals and data science to AI engineering, generative AI, cybersecurity and governance, Firebrand offers accelerated training and industry-recognised certifications to help you develop the skills for your chosen career path.

Explore Firebrand's AI courses and find the route that's right for you.